76 citations · 98 across the 8 of their papers we have counts for
5 papers · 1 filter
Expand Neurons, Not Parameters
Linghao Kong, Inimai Subramanian, Yonadav Shavit +3
This work demonstrates how increasing the number of neurons in a network without increasing its total number of non-zero parameters improves performance. We show that this gain cor…
Tools for Verifying Neural Models' Training Data
Dami Choi, Yonadav Shavit, David Duvenaud
It is important that consumers and regulators can verify the provenance of large neural models to evaluate their capabilities and risks. We introduce the concept of a "Proof-of-Tra…
What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring
Yonadav Shavit
As advanced machine learning systems' capabilities begin to play a significant role in geopolitics and societal order, it may become imperative that (1) governments be able to enfo…
Causal Strategic Linear Regression
Yonadav Shavit, Benjamin Edelman, Brian Axelrod
In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to "game" th…
Extracting Incentives from Black-Box Decisions
Yonadav Shavit, William S. Moses
An algorithmic decision-maker incentivizes people to act in certain ways to receive better decisions. These incentives can dramatically influence subjects' behaviors and lives, and…